Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy
Abstract: This paper describes a method for identifying topics in text published in social media, by...
Microblogging websites, such as Twitter, provide seemingly endless amount of textual information on ...
In this research, we extend probabilistic topic models, originally developed for a textual corpus an...
Topic classification of texts is one of the most interesting challenges in Natural Language Processi...
Topic classification of texts is one of the most interesting challenges in Natural Language Processi...
In Natural Language Processing, researchers design and develop algorithms to enable machines to unde...
International audienceIn this paper we introduce a novel low dimensional method to perform topic det...
Copyright © 2015 Duc-Thuan Vo et al. This is an open access article distributed under the Creative C...
Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for reveal...
The number of Twitter users is increasing and the quantity of produced data is growing. Using this b...
Abstract. Social media has become an effective channel for communicating both trends and public opin...
As large-scale digital text collections become abundant, the necessity of automatically summarizing ...
Recently, there has been an exponential rise in the use of online social media systems like Twitter ...
Columbia University, New YorkIn this technical report we introduce a novel low dimensional method to...
It is well known that supervised text classification methods need to learn from many labeled exampl...
Abstract: This paper describes a method for identifying topics in text published in social media, by...
Microblogging websites, such as Twitter, provide seemingly endless amount of textual information on ...
In this research, we extend probabilistic topic models, originally developed for a textual corpus an...
Topic classification of texts is one of the most interesting challenges in Natural Language Processi...
Topic classification of texts is one of the most interesting challenges in Natural Language Processi...
In Natural Language Processing, researchers design and develop algorithms to enable machines to unde...
International audienceIn this paper we introduce a novel low dimensional method to perform topic det...
Copyright © 2015 Duc-Thuan Vo et al. This is an open access article distributed under the Creative C...
Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for reveal...
The number of Twitter users is increasing and the quantity of produced data is growing. Using this b...
Abstract. Social media has become an effective channel for communicating both trends and public opin...
As large-scale digital text collections become abundant, the necessity of automatically summarizing ...
Recently, there has been an exponential rise in the use of online social media systems like Twitter ...
Columbia University, New YorkIn this technical report we introduce a novel low dimensional method to...
It is well known that supervised text classification methods need to learn from many labeled exampl...
Abstract: This paper describes a method for identifying topics in text published in social media, by...
Microblogging websites, such as Twitter, provide seemingly endless amount of textual information on ...
In this research, we extend probabilistic topic models, originally developed for a textual corpus an...